Outage Prediction Using Grid Segmentation and ML
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Solution Overview
Problem
Current methods for predicting electrical outages during severe weather events are inefficient, relying on guesswork and lacking quantitative data, leading to delayed and costly restoration processes for utility companies.
Innovation Solution
A system utilizing high-resolution weather forecasts combined with geographic and utility infrastructure data, employing machine learning models like Decision Trees, Random Forests, and Bayesian Additive Regression Trees to predict outages on a 2-km grid, allowing for pre-storm deployment of crews and resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional guesswork methods are used for predicting outages, then decision-making simplicity is maintained, but prediction accuracy and reliability deteriorate
Solution Approach 1:
The prediction system divides the service territory into discrete grid cells (e.g., 2km x 2km) and predicts outages independently for each cell. This segmentation allows the complex prediction problem to be broken into manageable units, each processed by the machine learning model using local weather, geographic, and infrastructure data, thereby improving overall prediction accuracy without overwhelming system complexity
Solution Approach 2:
The patent introduces machine learning models as an intermediary between raw data (weather forecasts, geographic information, infrastructure data) and outage predictions. These models process and synthesize multiple data sources to generate probabilistic outage predictions, transforming complex multi-source data into actionable insights without requiring direct human analysis of all variables
2Reliability
If quantitative data and machine learning models are used for outage prediction, then prediction reliability improves, but computational resources and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing geographic data, infrastructure data, and historical outage data in accessible formats before storm events. Machine learning models are trained in advance on historical data, so that during actual storm prediction, the system only needs to input forecast weather data and generate predictions quickly, reducing real-time computational burden while maintaining high reliability
Solution Approach 2:
The patent changes parameters by using probabilistic outputs and confidence intervals rather than deterministic predictions. The machine learning models generate probability distributions for outage occurrences, allowing utilities to make risk-based decisions. This parameter transformation enables reliable predictions with quantified uncertainty without requiring excessively complex computational models
3Measurement precision
If high-resolution weather forecasts and multiple data sources are integrated, then prediction precision improves, but data processing complexity increases
Solution Approach 1:
The patent creates a universal data framework that integrates multiple data sources (weather forecasts, geographic information, infrastructure data, historical outage data) into a common structure suitable for machine learning processing. This universal framework uses standardized data formats and consistent spatial referencing, allowing diverse data sources to be processed uniformly by the prediction models, improving precision without proportionally increasing integration complexity
Data Source
AI summary
A system and method for outage prediction for electrical distribution utilities using high-resolution weather forecasts, geographic data (e.g., land use and vegetation around overhead-lines) and utility infrastructure data (e.g., transformer fuses, etc.) to predict distributed outage occurrences (e.g., number of outages over a 2-km gridded map) in advance of a storm.